¿Son las GPUs dispositivos eficientes energéticamente?

With energy consumption emerging as one of the biggest issues in the development of HPC (High Performance Computing) applications, the importance of detailed power-related research works becomes a priority. In the last years, GPU coprocessors have been increasingly used to accelerate many of these high-priced systems even though they are embedding millions of transistors on their chips delivering an immediate increase on power consumption necessities. This paper analyzes a set of applications from the Rodinia benchmark suite in terms of CPU and GPU performance and energy consumption. Specifically, it compares single-threaded and multi-threaded CPU versions with GPU implementations, and characterize the execution time, true instant power and average energy consumption to test the idea that GPUs are power-hungry computing devices.Con el consumo de energía emergiendo como uno de los mayores problemas en el desarrollo de aplicaciones HPC (High Performance Computing), la importancia de trabajos específicos de investigación en este campo se convierte en una prioridad. En los últimos años, los coprocesadores GPU se han utilizado frecuentemente para acelerar muchos de estos costosos sistemas, a pesar de que incorporan millones de transistores en sus chips, lo que genera un aumento considerable en los requerimientos de energía. Este artículo analiza un conjunto de aplicaciones del benchmark Rodinia en términos de rendimiento y consumo de energía de CPU y GPU. Específicamente, se comparan las versiones secuenciales y multihilo en CPU con implementaciones GPU, caracterizando el tiempo de ejecución, la potencia real instantánea y el consumo promedio de energía, con el objetivo de probar la idea de que las GPU son dispositivos de baja eficiencia energética.Facultad de Informátic

¿Son las GPUs dispositivos eficientes energéticamente?

With energy consumption emerging as one of the biggest issues in the development of HPC (High Performance Computing) applications, the importance of detailed power-related research works becomes a priority. In the last years, GPU coprocessors have been increasingly used to accelerate many of these high-priced systems even though they are embedding millions of transistors on their chips delivering an immediate increase on power consumption necessities. This paper analyzes a set of applications from the Rodinia benchmark suite in terms of CPU and GPU performance and energy consumption. Specifically, it compares single-threaded and multi-threaded CPU versions with GPU implementations, and characterize the execution time, true instant power and average energy consumption to test the idea that GPUs are power-hungry computing devices.Con el consumo de energía emergiendo como uno de los mayores problemas en el desarrollo de aplicaciones HPC (High Performance Computing), la importancia de trabajos específicos de investigación en este campo se convierte en una prioridad. En los últimos años, los coprocesadores GPU se han utilizado frecuentemente para acelerar muchos de estos costosos sistemas, a pesar de que incorporan millones de transistores en sus chips, lo que genera un aumento considerable en los requerimientos de energía. Este artículo analiza un conjunto de aplicaciones del benchmark Rodinia en términos de rendimiento y consumo de energía de CPU y GPU. Específicamente, se comparan las versiones secuenciales y multihilo en CPU con implementaciones GPU, caracterizando el tiempo de ejecución, la potencia real instantánea y el consumo promedio de energía, con el objetivo de probar la idea de que las GPU son dispositivos de baja eficiencia energética.Facultad de Informátic

GPU Performance and Power Consumption Analysis: A DCT based denoising application

It is known that energy and power consumption are becoming serious metrics in the design of high performance workstations because of heat dissipation problems. In the last years, GPU accelerators have been integrating many of these expensive systems despite they are embedding more and more transistors on their chips producing a quick increase of power consumption requirements. This paper analyzes an image processing application, in particular a Discrete Cosine Transform denoising algorithm, in terms of CPU and GPU performance and energy consumption. Specifically, we want to compare single-threaded and multithreaded CPU versions with a GPU version, and characterize the execution time, true instant power and average energy consumption to deflate the idea that GPUs are non-green computing devices.XVIII Workshop de Procesamiento Distribuido y Paralelo (WPDP).Red de Universidades con Carreras en Informática (RedUNCI

Power Aware Computing on GPUs

Energy and power density concerns in modern processors have led to significant computer architecture research efforts in power-aware and temperature-aware computing. With power dissipation becoming an increasingly vexing problem, power analysis of Graphical Processing Unit (GPU) and its components has become crucial for hardware and software system design. Here, we describe our technique for a coordinated measurement approach that combines real total power measurement and per-component power estimation. To identify power consumption accurately, we introduce the Activity-based Model for GPUs (AMG), from which we identify activity factors and power for microarchitectures on GPUs that will help in analyzing power tradeoffs of one component versus another using microbenchmarks. The key challenge addressed in this thesis is real-time power consumption, which can be accurately estimated using NVIDIA\u27s Management Library (NVML) through Pthreads. We validated our model using Kill-A-Watt power meter and the results are accurate within 10\%. The resulting Performance Application Programming Interface (PAPI) NVML component offers real-time total power measurements for GPUs. This thesis also compares a single NVIDIA C2075 GPU running MAGMA (Matrix Algebra on GPU and Multicore Architectures) kernels, to a 48 core AMD Istanbul CPU running LAPACK

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI